Bayesian color estimation for adaptive vision-based robot localization

In this article we introduce a hierarchical Bayesian model to estimate a set of colors with a mobile robot. Estimating colors is particularly important if objects in an environment can only be distinguished by their color. Since the appearance of colors can change due to variations in the lighting condition, a robot needs to adapt its color model to such changes. We propose a two level Gaussian model in which the lighting conditions are estimated at the upper level using a switching Kalman filter. A hierarchical Bayesian technique learns Gaussian priors from data collected in other environments. Furthermore, since estimation of the color model depends on knowledge of the robot's location, we employ a Rao-Blackwellised particle filter to maintain a joint posterior over robot positions and lighting conditions. We evaluate the technique in the context of the RoboCup AIBO league, where a legged AIBO robot has to localize itself in an environment similar to a soccer field. Our experiments show that the robot can localize under different lighting conditions and adapt to changes in the lighting condition, for example, due to a light being turned on or off.

Authors

• Dirk Schulz 0001
• Dieter Fox

Keywords

• Bayesian methods
• Robot localization
• Robot sensing systems
• Robot vision systems
• State estimation
• Mobile robots
• Cameras
• Computer science
• Particle filters
• Gaussian distribution
• Bayesian Model
• Light Conditions
• Hierarchical Model
• Gaussian Model
• Kalman Filter
• Particle Filter
• Mobile Robot
• Color Model
• Color Appearance
• Position Of The Robot
• Hierarchical Technique
• Soccer Field
• Environmental Conditions
• Light Source
• K-means
• Levels In Model
• Current Position
• Sudden Changes
• Lookup Table
• Changing Light Conditions
• Camera Position
• Color Categories
• Environment Map
• Color Map
• Camera Images
• Mean Vector
• Current Image
• Varying Lighting Conditions
• Independent Gaussian